Machine Learning (ML) based predictive models have made rapid strides in computational chemistry due to their efficiency and performance. Characterized by their computational efficiency and accuracy, these methods are capable of faster high-throughput screening compared to classical physics models [1]}, [2]}. This capability has roots in both novel learning algorithms and improved hardware. Even though ML models can offer faster predictions, the accuracy of these models is highly correlated with the availability of clean labeled data. [3]}. In general, it is difficult to develop accurate and robust ML models without sufficiently large labeled data [4]}. Moreover, the acquisition of labeled data is expensive as it involves performing Density Functional Theory (DFT) simulations or experiments to characterize materials [5]}, [6]}. On the other hand, gigantic databases containing structures and compositions of materials without labels (property) are available. These databases can not be used in supervised learning tasks due to the lack of labels. Given the availability of large unlabeled datasets, some interesting questions can be asked 1.) can we develop more efficient ML models that are capable of learning the underlying structural chemistry from unlabeled data and 2.) can these models be used to make the supervised learning tasks more accurate?
